Predicting breast cancer recurrence and metastasis risk by integrating color and texture features of histopathological images and machine learning technologies

Author:

Liu Xinyu,Yuan Peng,Li Ruolin,Zhang Dejun,An Junda,Ju Jie,Liu Chenyang,Ren Fuquan,Hou RuiORCID,Li Yushuang,Yang JialiangORCID

Funder

National Natural Science Foundation of China

Hebei Provincial Natural Science Foundation

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference43 articles.

1. Evaluating DNA methylation, gene expression, somatic mutation, and their combinations in inferring tumor tissue-of-origin;Liu;Front. Cell Dev. Biol.,2021

2. Hazard of recurrence among women after primary breast cancer treatment—a 10-year follow-up using data from SEER-Medicare;Cheng;Cancer Epidemiology and Prevention Biomarkers,2012

3. TOOme: a novel computational framework to infer cancer tissue-of-origin by integrating both gene mutation and expression;He;Front. Bioeng. Biotechnol.,2020

4. Unfavourable pattern of metastases in M0 breast cancer patients during 1978-2008: a population-based analysis of the Munich Cancer Registry;van den Hurk;Breast Cancer Res. Treat.,2011

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